Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

08/09/2016

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by   Nicholas Locascio, et al.

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This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does not utilize domain-specific crafting, learning to translate directly from a parallel corpus. To fully explore the potential of neural models, we propose a methodology for collecting a large corpus of regular expression, natural language pairs. Our resulting model achieves a performance gain of 19.6